Every retailer knows that product position within their store has a direct influence on revenue. But how do you apply this knowledge? Would you win more by selling a batch of cheaper goods or a few high priced items? Will the product placed on the same position in two different outlets get equal attention from the consumers?
DB Best leveraged neural networks and machine learning algorithms to develop a solution that allows for increasing sales and doubling the retailer’s revenue for a certain store.
Four reasons for using a neural network model
Before development started, our data scientists drilled down into research to assure — utilizing neural networks is the most effective solution for revenue forecasting. The approach proved to be advantageous for several reasons:
- The neural network model captures deep and unobvious factors based on many parameters.
- It captures deep patterns that cannot be found using human analysis.
- The model has high accuracy in predicting what kind of profit the goods will bring.
- As a result, we receive a unique list of products for each store that will bring the most profit.
Thus, the forecasting algorithm provides for in-depth study that reveals the smallest details that influence sales of a particular outlet.
Using the neural network for revenue forecasting
Our data scientists concentrated on the rifle approach. They suggested a solution to increase revenue for certain stores and thus to increase it for the whole chains. The team trained a neural network to build revenue forecasts and define goods that bring profit for particular stores.
We aggregated the data that our customers supplied on one of their shelves. The pool included varied codified data on goods on that shelf such a product name, location, brand, etc. As well, there figured details on the store itself.
Our developers gained the advantage of neural networks and built an ML model. Then, we filled our model with data.
We utilized best practices and filled data into the neural network in curves. So, the math model receives data as several categorical variables and the consistency of received patterns significantly increases.
The team analyzed that data to find common patterns and built an ML model. We filled data into the neural network in curves, which is a best practice that allows for increasing the consistency of patterns received in the end. Afterward, we trained the ML model to forecast revenue.
This is how the solution works
Let’s consider an upmarket standing on a busy street in the center of Washington. We would input the data on that store’s shelf into the ML model to find common patterns. Then we would train the neural network on the information on goods sitting on that shelf including brands, pricing, positioning, how often customers buy those goods, and so on. Even the smallest details matter.
Based on that data, the math model calculates patterns that define the consumer’s behavior.
With the model, we see what goods customers buy more often and what goods they buy less, though these sales ensure retailer higher profit.
Processing these insights the ML model assembles a list of the most profitable goods for a shelf.
Though not obvious prior to the ML model, the most sellable and most profitable goods were not the same goods. Importantly, it all depends on the behavior and preferences of consumers.
Our forecasting model allows for the creation of a unique list of goods for each store that will result in the highest profit for that store. We can’t share the original code, however, here is the synthetic example of how to create and train the neural network.
The forecasting prospects
The study our team conducted for our customers showed the high accuracy of our solution. As proven by the results, the forecast reliability is quite high ranging from 96 and 98%.
Our analysts tested the solution in the lab and in the field and came up with the following numbers. Under the “in the lab” condition, revenue increased by 20 times. “In the field”, revenue was at the mean 2-4 times higher than average.
This variance results from the promotional events held in certain stores, and advertising banners placed in the area of a certain shelf. Loyalty systems influence sales. According to our study, if we trained the neural networks regarding the data on special offers and the specifics of the customer’s loyalty system, we could increase sales by 10 times.
Benefits of using machine learning in retail
Hence, we have designed a recommender engine that builds retailer’s personalized plans on how to increase the profit from their outlets based on insights into their sales data. As our customers have observed an expected revenue increase and became interested in further developing the model, our data science team keeps on track.
The next step for our developers is to double down on training the neural networks model with total information to gain even better efficiency.
The recommender engine is a good solution for retailers who don’t have in their team analysts responsible for finding patterns meant to improve the sales rates. Besides, the more parameters we use to train the model the better results we receive. Thus, according to our research applying marketing data let us increase sales by 10 times or more.
See what we offer
The DB Best team of data scientists developed a number of solutions that brought our customers profit and gave the upper hand in relation to their competitors.
Thus, our team’s potential is not limited to ready-made solutions. We cherish each client’s aims and legacy, and so, the solutions we build are always count-solutions. Contact us for a reliable analysis to know how the latest technologies can make the most profit out of your business solution.